Molecular design with Pretrained SAFE

In [1]:

%load_ext autoreload
%autoreload 2

%load_ext autoreload %autoreload 2

In [1]:

import os


os.environ["TOKENIZERS_PARALLELISM"] = "false"


import safe as sf
import datamol as dm

import os os.environ["TOKENIZERS_PARALLELISM"] = "false" import safe as sf import datamol as dm

In [2]:

# designer = sf.SAFEDesign.load_default(verbose=True)

# designer = sf.SAFEDesign.load_default(verbose=True)

---------------------------------------------------------------------------
HFValidationError                         Traceback (most recent call last)
/home/hadim/Code/valence/Libs/safe/docs/tutorials/design-with-safe.ipynb Cell 3 line 1
----> <a href='vscode-notebook-cell:/home/hadim/Code/valence/Libs/safe/docs/tutorials/design-with-safe.ipynb#W4sZmlsZQ%3D%3D?line=0'>1</a> designer = sf.SAFEDesign.load_default(verbose=True)

File ~/Code/valence/Libs/safe/safe/sample.py:89, in SAFEDesign.load_default(cls, verbose, model_dir, device)
     87 if model_dir is None or not model_dir:
     88     model_dir = cls._DEFAULT_MODEL_PATH
---> 89 model = SAFEDoubleHeadsModel.from_pretrained(model_dir)
     90 tokenizer = SAFETokenizer.load(os.path.join(model_dir, "tokenizer.json"))
     91 gen_config = GenerationConfig.from_pretrained(model_dir)

File ~/local/micromamba/envs/safe/lib/python3.11/site-packages/transformers/modeling_utils.py:2507, in PreTrainedModel.from_pretrained(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs)
   2504 if commit_hash is None:
   2505     if not isinstance(config, PretrainedConfig):
   2506         # We make a call to the config file first (which may be absent) to get the commit hash as soon as possible
-> 2507         resolved_config_file = cached_file(
   2508             pretrained_model_name_or_path,
   2509             CONFIG_NAME,
   2510             cache_dir=cache_dir,
   2511             force_download=force_download,
   2512             resume_download=resume_download,
   2513             proxies=proxies,
   2514             local_files_only=local_files_only,
   2515             token=token,
   2516             revision=revision,
   2517             subfolder=subfolder,
   2518             _raise_exceptions_for_missing_entries=False,
   2519             _raise_exceptions_for_connection_errors=False,
   2520         )
   2521         commit_hash = extract_commit_hash(resolved_config_file, commit_hash)
   2522     else:

File ~/local/micromamba/envs/safe/lib/python3.11/site-packages/transformers/utils/hub.py:429, in cached_file(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)
    426 user_agent = http_user_agent(user_agent)
    427 try:
    428     # Load from URL or cache if already cached
--> 429     resolved_file = hf_hub_download(
    430         path_or_repo_id,
    431         filename,
    432         subfolder=None if len(subfolder) == 0 else subfolder,
    433         repo_type=repo_type,
    434         revision=revision,
    435         cache_dir=cache_dir,
    436         user_agent=user_agent,
    437         force_download=force_download,
    438         proxies=proxies,
    439         resume_download=resume_download,
    440         token=token,
    441         local_files_only=local_files_only,
    442     )
    443 except GatedRepoError as e:
    444     raise EnvironmentError(
    445         "You are trying to access a gated repo.\nMake sure to request access at "
    446         f"https://huggingface.co/{path_or_repo_id} and pass a token having permission to this repo either "
    447         "by logging in with `huggingface-cli login` or by passing `token=<your_token>`."
    448     ) from e

File ~/local/micromamba/envs/safe/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py:110, in validate_hf_hub_args.<locals>._inner_fn(*args, **kwargs)
    105 for arg_name, arg_value in chain(
    106     zip(signature.parameters, args),  # Args values
    107     kwargs.items(),  # Kwargs values
    108 ):
    109     if arg_name in ["repo_id", "from_id", "to_id"]:
--> 110         validate_repo_id(arg_value)
    112     elif arg_name == "token" and arg_value is not None:
    113         has_token = True

File ~/local/micromamba/envs/safe/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py:158, in validate_repo_id(repo_id)
    155     raise HFValidationError(f"Repo id must be a string, not {type(repo_id)}: '{repo_id}'.")
    157 if repo_id.count("/") > 1:
--> 158     raise HFValidationError(
    159         "Repo id must be in the form 'repo_name' or 'namespace/repo_name':"
    160         f" '{repo_id}'. Use `repo_type` argument if needed."
    161     )
    163 if not REPO_ID_REGEX.match(repo_id):
    164     raise HFValidationError(
    165         "Repo id must use alphanumeric chars or '-', '_', '.', '--' and '..' are"
    166         " forbidden, '-' and '.' cannot start or end the name, max length is 96:"
    167         f" '{repo_id}'."
    168     )

HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/home/hadim/.cache/safe/default_model'. Use `repo_type` argument if needed.

In [30]:

# candidate_mol = "O=C(C#CCN1CCCCC1)Nc1ccc2ncnc(Nc3cccc(Br)c3)c2c1"

# scaffold = "[*]N-c1ccc2ncnc(-N[*])c2c1" # this is for scaffold decoration
# superstructure = "c1ccc2ncncc2c1"
# side_chains = '[1*]C(=O)C#CCN1CCCCC1.[2*]c1cccc(Br)c1' # this is for scaffold morphing
# motif = "[*]-N1CCCCC1" # this is for motif extension
# linker_generation = ["[*]-N1CCCCC1", "Brc1cccc(Nc2ncnc3ccc(-[*])cc23)c1"] # this is for linker generation

# candidate_mol = "O=C(C#CCN1CCCCC1)Nc1ccc2ncnc(Nc3cccc(Br)c3)c2c1" # scaffold = "[*]N-c1ccc2ncnc(-N[*])c2c1" # this is for scaffold decoration # superstructure = "c1ccc2ncncc2c1" # side_chains = '[1*]C(=O)C#CCN1CCCCC1.[2*]c1cccc(Br)c1' # this is for scaffold morphing # motif = "[*]-N1CCCCC1" # this is for motif extension # linker_generation = ["[*]-N1CCCCC1", "Brc1cccc(Nc2ncnc3ccc(-[*])cc23)c1"] # this is for linker generation

We start with the image shown in the README of the SAFE GitHub Repository to assess the performance on the various tasks SAFE should in theory be good at.

In [31]:

# dm.to_image(dm.to_mol(candidate_mol))

# dm.to_image(dm.to_mol(candidate_mol))

Out[31]:

In [32]:

# N_SAMPLES = 100

# N_SAMPLES = 100

De novo generation¶

Generation of novel molecules without any constraints.

In [33]:

# generated = designer.de_novo_generation(sanitize=True, n_samples_per_trial=N_SAMPLES)

# generated = designer.de_novo_generation(sanitize=True, n_samples_per_trial=N_SAMPLES)

2023-08-29 16:58:21.338 | INFO     | safe.sample:de_novo_generation:559 - After sanitization, 83 / 100 (83.00 %)  generated molecules are valid !

In [34]:

# dm.to_image(generated[:20])

# dm.to_image(generated[:20])

Out[34]:

Scaffold Decoration¶

For scaffold decoration, we wish to generate new molecules that would contain a given scaffold as core. Usually, the attachment point on the scaffold should dictate where the new vectors will be added.

In [35]:

# dm.to_image(scaffold)

# dm.to_image(scaffold)

Out[35]:

In [36]:

# generated = designer.scaffold_decoration(scaffold=scaffold, n_samples_per_trial=N_SAMPLES, n_trials=2, sanitize=True, do_not_fragment_further=True)

# generated = designer.scaffold_decoration(scaffold=scaffold, n_samples_per_trial=N_SAMPLES, n_trials=2, sanitize=True, do_not_fragment_further=True)

2023-08-29 16:59:49.566 | INFO     | safe.sample:scaffold_decoration:534 - After sanitization, 182 / 200 (91.00 %)  generated molecules are valid !

In [37]:

# dm.viz.lasso_highlight_image([dm.to_mol(x) for x in generated[:20]], dm.from_smarts(scaffold))

# dm.viz.lasso_highlight_image([dm.to_mol(x) for x in generated[:20]], dm.from_smarts(scaffold))

Out[37]:

Super structure generation¶

In super structure generation, we just want to generate superstructure of a molecular subgraph

In [38]:

# dm.to_image(superstructure)

# dm.to_image(superstructure)

Out[38]:

In [39]:

# generated = designer.super_structure(core=superstructure, n_samples_per_trial=N_SAMPLES, n_trials=1, sanitize=True, do_not_fragment_further=False, attachment_point_depth=3)
# #generated

# generated = designer.super_structure(core=superstructure, n_samples_per_trial=N_SAMPLES, n_trials=1, sanitize=True, do_not_fragment_further=False, attachment_point_depth=3) # #generated

2023-08-29 16:59:56.491 | INFO     | safe.sample:super_structure:488 - After sanitization, 41 / 100 (41.00 %)  generated molecules are valid !

In [40]:

# dm.to_image(generated[:20])

# dm.to_image(generated[:20])

Out[40]:

Motif Extension¶

In motif extension, we are interested in generating a molecule containing a given motif as starting point.

In [41]:

# dm.to_image(motif)

# dm.to_image(motif)

Out[41]:

In [48]:

# # let's make some long sequence
# generated = designer.motif_extension(motif=motif, n_samples_per_trial=N_SAMPLES, n_trials=1, sanitize=True, do_not_fragment_further=False, min_length=25, max_length=80)

# # let's make some long sequence # generated = designer.motif_extension(motif=motif, n_samples_per_trial=N_SAMPLES, n_trials=1, sanitize=True, do_not_fragment_further=False, min_length=25, max_length=80)

2023-08-29 17:01:55.965 | INFO     | safe.sample:scaffold_decoration:534 - After sanitization, 98 / 100 (98.00 %)  generated molecules are valid !

In [49]:

# dm.to_image(generated[:20])

# dm.to_image(generated[:20])

Out[49]:

Scaffold Morphing¶

In scaffold morphing, we wish to replace a scaffold by another one in a molecule. The process requires as input that the user provides either the side chains or the input molecules and the core

In [44]:

# dm.to_image(side_chains)

# dm.to_image(side_chains)

Out[44]:

In [45]:

# generated = designer.scaffold_morphing(side_chains=side_chains, n_samples_per_trial=N_SAMPLES, n_trials=1, sanitize=True, do_not_fragment_further=False, random_seed=100)
# dm.to_image(generated[:20])

# generated = designer.scaffold_morphing(side_chains=side_chains, n_samples_per_trial=N_SAMPLES, n_trials=1, sanitize=True, do_not_fragment_further=False, random_seed=100) # dm.to_image(generated[:20])

2023-08-29 17:00:14.942 | INFO     | safe.sample:_fragment_linking:389 - After sanitization, 100 / 100 (100.00 %)  generated molecules are valid !

Out[45]:

Linker generation¶

Linker generation is mostly the same thing as scaffold morphing ...

In [46]:

# dm.to_image(linker_generation)

# dm.to_image(linker_generation)

Out[46]:

In [47]:

# generated = designer.linker_generation(*linker_generation, n_samples_per_trial=N_SAMPLES, n_trials=1, sanitize=True, do_not_fragment_further=False, random_seed=100)
# dm.to_image(generated[:20])

# generated = designer.linker_generation(*linker_generation, n_samples_per_trial=N_SAMPLES, n_trials=1, sanitize=True, do_not_fragment_further=False, random_seed=100) # dm.to_image(generated[:20])

2023-08-29 17:00:27.044 | INFO     | safe.sample:_fragment_linking:389 - After sanitization, 100 / 100 (100.00 %)  generated molecules are valid !

Out[47]: